GradDFT is a JAX-based library enabling the differentiable design and experimentation of exchange-correlation functionals using machine learning techniques.
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Implement a less computationally demanding version of LYP #53
Right now GradDDFT implements the original expression for the LYP functional, eq 22 in
but there is a more computationally efficient one, eq 2 in
This is low priority however.